Statistical smoothing of neuronal data
Statistical smoothing of neuronal data
复制标题
DOI：
10.1088/0954-898x/14/1/301
复制
发表时间：
2003-02-01
影响因子：
7.8
通讯作者：
Cai, C
中科院分区：
文献类型：
--
作者：
Kass, RE;Ventura, V;Cai, C
平滑（过滤）神经元数据的目的是改进瞬时放电率的估计。在一些应用中，科学兴趣集中在瞬时放电率的函数上，例如最大放电率发生的时间或在一些实验相关时期内放电率的增加率。在其他情况下，基于概率的计算需要瞬时发射率。在本文中，我们指出与使用周刺激时间直方图 (PSTH) 相比，统计效率泡沫平滑方法具有非常显着的增益，并且我们还演示了一种新的自适应平滑方法，称为贝叶斯自适应回归样条（DiMatteo I、Genovese C R 和 Kass R E 2001 Biometrika 88 1055-71）。我们简要回顾一下非泊松过程平滑以及在一对神经元的联合 PSTH 中的其他应用。
The purpose of smoothing (filtering) neuronal data is to improve the estimation of the instantaneous firing rate. In some applications, scientific interest centres on functions of the instantaneous firing rate, such as the time at which the maximal firing rate occurs or the rate of increase of firing rate over some experimentally relevant period. In others, the instantaneous firing rate is needed for probability-based calculations. In this paper we point to the very substantial gains in statistical efficiency froth smoothing methods compared to using the peristimulus-time histogram (PSTH), and we also demonstrate a new method of adaptive smoothing known as Bayesian adaptive regression splines (DiMatteo I, Genovese C R and Kass R E 2001 Biometrika 88 1055-71). We briefly review additional applications of smoothing with non-Poisson processes and in the joint PSTH for a pair of neurons.